The Unreasonable Effectiveness of Separating the Task from the Model — Maxime Rivest & Isaac Miller
Jul 23, 2026 · 17:11
Maxime Rivest and Isaac Miller, core contributors to the open-source DSPy framework, argue that AI programs should separate task specification from model implementation using signatures—fixed input/output contracts. They detail three components of a task: instructions (what should happen), code (constraints), and evals (what good looks like), enabling automatic optimization. Enterprise case studies like Shopify achieving 550x cost reduction by swapping expensive models for cheap ones while keeping evals constant demonstrate practical gains. DSPy 3.5 and 4.0 introduce new techniques including Recursive Language Models (RLMs) for long-context tasks, DSPy.flex for learning harnesses that generate code, and Qualitative Learning for converting production feedback into evals. The speakers emphasize that even with AGI, models will need to learn business-specific context through this last-mile learning approach.
